Received: 4 August 2011 Accepted: 4 October 2011 Published online: 30 October 2011

Abstract

Background

Skeletal muscle mass (SMM) can be extracted from whole-body scans obtained by X-ray-based dual-photon absorptiometry (DXA).
There is a need to establish expected age-dependent values for children and adolescents.

Methods

Appendicular
lean tissue mass (ALM) was extracted from whole-body DXA scans in 140
healthy children and adolescents (68 females
and 72 males). Whole-body SMM was calculated from ALM
using equations developed by Kim et al. (Am J Clin Nutr 84:1014–1020,
2006). Age-dependent
patterns of increase in SMM were derived by fitting SMM values to
equations that consisted of the sum of
two logistic expressions, one accounting for SMM
changes during growth and the other for SMM changes during puberty.
Normal
ranges were defined so that 95% of the SMM values were
included. The reproducibility of SMM measurements was obtained from
whole-body DXA scans repeated on three occasions in
each of a separate group of 32 normal children with repositioning
between
scans.

Results

Normal
ranges are presented as equations describing the age-dependent pattern
of increase in SMM as well as population standard
deviations that increased steadily with age. For 15
children below age 10, SMM reproducibility (95% CI) was 149 g
(119–199 g)
while for 17 children and adolescents over age 10,
reproducibility was 170 g (138–223 g).

Conclusion

DXA-based measurements of SMM in children and adolescents are reproducible and can be expressed in terms of age-dependent
Z scores.

Heymsfield et al. [1] first showed in adults that ALM, the lean tissue mass in the arms and legs, when measured using radioisotope-based dual-photon
absorptiometry (DPA) was strongly related to both total body potassium, estimated by whole-body counting of 40K,
and to total body nitrogen measured by neutron activation. It followed
that DPA could form the basis of a technique for
the measurement of SMM, the component of muscle tissue
that is used to effect locomotion and to maintain posture, especially
since it was known that ALM constitutes between 73% and
75% of SMM. The technique was validated [2]
when whole-body SMM was determined using MRI in a large group of
healthy adults and compared with ALM measured by dual-energy
X-ray absorptiometry (DXA). The derived relationship was
whole-body SMM = (1.19 × ALM) − 1.01, and ALM explained 96% of the
variance in whole-body SMM. This relationship was refined
by accounting for the intermuscular adipose tissue that had been
included previously so that the predictive relationship
in adults was adjusted to become SMM = (1.19 × ALM) − 1.65 [3]. Again, 96% of the variance in whole-body SMM was explained by the variance in ALM.

The technique was extended to children by comparing MRI measures of SMM with DXA measures of appendicular lean tissue mass
[4]. It was first shown
that for adolescents at Tanner stage 5 and beyond, the adult predictive
equation applied. However, for
younger children, the adult equation was inadequate.
Using regression techniques, a prediction model was developed in 65
children
who were less than Tanner stage 5. The predictive
equation for such children was SMM = (1.115 × ALM) − 1.135 [4].
This equation was tested in an independent sample of 18 children in
whom it was found that ALM was still the strongest
predictor of SMM accounting for 98% of the variance.
Addition of weight and height as predictor variables added a small but
significant improvement in SMM prediction.

The purpose of our study was to apply the
above predictive equations in a population of normal Canadian children
and adolescents
in order to establish ranges for expected values of SMM.
We also evaluated the reproducibility of SMM estimations from repeated
measures of ALM in a separate group of children.

2 Materials and methods

2.1 Subjects and DXA measurement

Whole-body DXA scans were obtained
using a Hologic densitometer (4500A or Discovery A) for 140 children
and adolescents (68
females and 72 males) between the ages of 3.1 and
18.8 years. These ostensibly normal local children and adolescents
were
free of any known disease that might affect bone or
body composition. Originally, these healthy volunteers had been
recruited
to establish normal reference ranges for whole-body
areal bone mineral density (BMD) and body composition in Canadian
children
and adolescents [5]. A representative scan is shown in Fig. 1.
Each scan was reanalyzed by a single investigator to ensure that
regional markers were positioned systematically. All measurements
had been approved by the Research Ethics Board of
Hamilton Health Sciences and McMaster University, and all volunteers
provided
informed consent before any DXA scans were
obtained.

Skeletal muscle mass was measured using the predictive equations developed by Kim et al. [4].
In brief, from the whole-body scan, the lean tissue mass assigned to
the arms and legs was summed. SMM was calculated for
each child using one of the following
relationships. For those at Tanner stage 5 and beyond, the equation was

(1)

For children yet to achieve Tanner stage 5, the equation was

(2)

For our whole-body DXA scans in volunteer normal children and adolescents, the Tanner stage was unknown. Based on the results
of assessments of sexual maturation in US children [6],
it was assumed that all children aged 16 and beyond had achieved Tanner
stage 5 and that the first equation above was appropriate.
The second equation was applied to all children
aged 15 or below.

2.3 Normal ranges of skeletal muscle mass

The derived values of SMM were
plotted as a function of age and gender. Equations analogous to those
previously developed
to describe the age dependence of whole-body bone
mineral content, lean body mass, and fat mass in children and
adolescents
[5] were fitted to the SMM values. These equations had the form

(3)

where A–F are constants derived for each
gender. The first term represents the steady increase in SMM associated
with growth
while the second term describes the rapid change in
SMM associated with puberty. The constants A and D represent the
respective
contributions to the SMM at age 20 arising from
growth and puberty. At younger ages, the growth contribution is modified
by
the exponential term that includes the age of the
subject. The pubertal contribution is modified by the exponential term
that
includes postpubertal age (Age-F). Normal ranges
were defined using the expression

(4)

where G and H are constants
which were adjusted so that 95% of the SMM values were included within
the normal range. The value 1.99 corresponds
to the 95% probability of a two-tailed t distribution for 70 df.

2.4 Reproducibility of skeletal muscle mass

A separate group of 32 normal children
and adolescents (18 females and 14 males) aged between 3.7 and
17.7 years underwent
three consecutive whole-body DXA scans with
repositioning between scans. These scans, which were reanalyzed by the
same investigator
to assess reproducibility of DXA-based estimates of
SMM, had been used previously for the assessment of reproducibility of
whole-body BMD and body composition in children and
adolescents [7]. Ninety-five percent confidence intervals for reproducibility were calculated assuming a chi-square distribution for the
individual variances found for each child [8].

3 Results

Figure 2
shows the calculated expected-for-age values of SMM for the 68 normal
female children and adolescents. The values assigned
to the parameters of the equation describing the
pattern of increase in SMM with age in females are given in Table 1. The constants A and D
reflect the increases in SMM that can be attributed to growth and to
puberty respectively. In girls, approximately 65% of
SMM was acquired during the pubertal growth spurt
which occurred at around age 11.5 years (constant F). Figure 3 presents the corresponding results for the 72 normal male children and adolescents. The values assigned to the parameters
of the equation describing the pattern of increase in SMM with age in males are given in Table 1. In boys, approximately 61% of SMM was acquired during the pubertal growth spurt which occurred at around age 13.7 years.

The results for SMM reproducibility were
considered separately for children above and below the age of
10 years. There were
15 younger children (nine females and six males). Their
mean age (±standard deviation), height, and weight were
8.1 ± 1.6 years,
129.5 ± 10.7 cm, and 27.4 ± 6.1 kg,
respectively. The precision for the younger group was 149 g with a
95% confidence interval
of 119–199 g. The older group consisted of 17
children (nine females and eight males). Their mean age, height, and
weight
were 12.6 ± 2.0 years, 154.7 ± 15.3 cm, and
45.0 ± 12.5 kg, respectively. The precision for the older group was
170 g with
a 95% confidence interval of 138–223 g. If all
children were considered as a single group, the reproducibility was
161 g with
a 95% confidence interval of 137–194 g.

Figure 2
also shows the normal range for SMM in girls. For one female, SMM was
approximately 2 kg above the upper limit of the derived
normal range while for another, SMM was about
2 kg below the lower limit. In total, three of 68 results (4.4%)
were excluded
from the selected normal range. As shown in Fig. 3, a similar outcome was obtained for males with four of 72 results (5.6%) excluded from the normal range. The values assigned
to the parameters that define the upper and lower limits of the normal ranges are given in Table 2. The positive values of the constant H indicates a steadily expanding normal range as age increases with a more rapid expansion of the normal range in males.

Table 2 Parameter values for the equations describing the age dependence of the normal range of values for SMM in female and male
children and adolescents

G

H

Female

0.10

0.16

Male

0.005

0.325

4 Discussion

A number of methods for the measurement of SMM in children have been evaluated with the objective of providing accurate, precise
results in the clinical setting. Poortmans et al. [9]
evaluated the merits of estimating SMM based on readily accessible
variables in non-obese subjects. They measured SMM using
the whole-body DXA technique in 39 children and 20 adults
and examined regression relationships between the measured value
and a predicted value based upon a group of variables
that included height, age, and sex as well as skinfold thickness and
limb circumference at each of the mid-arm, mid-thigh, and
mid-calf sites; a coefficient of correlation (r2 value) of 0.966 was observed. When the DXA-based SMM assessments were correlated with 24 h urine creatinine excretions in
the same group of subjects, the r2 value fell to 0.73. The predictive equations were not tested in an independent group of subjects.

Wang et al. [10] explored the correlation between MRI determined SMM and total body potassium determined from whole-body counting of the
naturally occurring radioisotope 40K in 116
healthy children aged 5 to 17 years. SMM in children was shown to
be a smaller fraction of total body potassium than
in adults; in adults, the fraction is constant and
independent of age [11]. Factors in addition to total body potassium that slightly improved the prediction of SMM in children were weight, height,
and race.

The technique for the derivation of total body SMM from the mass of lean tissue in the arms and legs as measured by DPA was
developed in adults [1] and was then extended to children [4].
The use of DXA scanning to measure ALM and hence to estimate SMM was
validated by comparison with direct measures of SMM
using whole-body MRI. DXA-based estimates of SMM are
likely to be more acceptable than estimates of SMM based on whole-body
MRI scans because of: (1) the difficulty of access to MRI
scanners in comparison to DXA scanners; (2) the expense associated
with MRI scanning; and (3) the need for image processing
to segment MRI images.

The DXA-based technique for estimation of
SMM has a reproducibility of 161 g. Expressed as a coefficient of
variation, the
reproducibility is 1.4%. However, it should be noted that
expression of reproducibility in terms of a coefficient of variation
is inappropriate, especially in children. When assessed
for children below age 10, the reproducibility was 149 g; for
children
over age 10, the reproducibility was 170 g. When
expressed as a CV, reproducibility in the younger children appeared to
be
worse at 1.8% compared to a value of 1.0% in the older
children. However, despite an overlap of 95% confidence intervals for
the standard deviations in the two groups of children,
numerically, precision was better in the younger children. For a child
aged 8, the age-expected value of SMM is about
8–10 kg. With a precision of 149 g, the uncertainty in the
difference between
two successive measurements will be (1492 + 1492)1/2 or 211 g. To be 95% confident that a change had occurred between the two measurements, the difference would have to exceed
(1.97 × 211) or 413 g.

A gender specific, age-expected value of SMM for a child between the ages of 3 and 18 years (SMMpredicted) can be calculated using Eq. 3 with the appropriate values provided in Table 1. A measured value (SMMmeasured) can then be expressed in terms of a Z-score by deriving the appropriate, age-dependent standard deviation from the data given in Table 2 and using the following equation. That is,

(5)

The derived Z-score can be used to interpret a measurement of SMM in terms of an expected value based on age and gender.

A limitation of our study is that Eqs. 1 and 2 were developed using whole-body scans obtained from a Lunar Densitometer [10]. The whole-body DXA scans for our normal subjects were obtained from a Hologic Densitometer. Software differences between
the two manufacturers will likely mean that the values of the slope and intercept in Eqs. 1 and 2 need to be adjusted for Hologic equipment. However, this will have little, if any, effect on the Z-scores calculated from Eq. 5 since the measured SMM and the SMM predicted for age will both include the same systematic error. Since the difference between
these two variables is required for the Z-score
calculation, the influence of the systematic error is minimized. This
conclusion needs to be validated by comparing,
in a group of children and adolescents, absolute SMM
measured with whole-body MRI and SMM values determined from ALM measured
on an Hologic densitometer. Another criticism might be
that Tanner stages for our normal population were unknown. The influence
of this effect can be estimated. If, for example, a
13-year-old female had a measured ALM of 17 kg, our procedure would
have
estimated her SMM from Eq. 2 as 17.82 kg. Since she was, in fact, at Tanner stage 5, Eq. 1
should have been used which would have yielded a SMM of 18.58 kg.
The difference between these values is somewhat greater
than the smallest detectable change for the DXA technique
of SMM measurement but is very much smaller than the normal ranges
established for boys and girls. Finally, it must be
stressed that the prediction equations presented here apply only to
children
and adolescents below age 20 and do not account for the
expected increases in SMM beyond that age.

This work has provided a means of
interpreting DXA-based measurements of SMM in children and adolescents
in terms of expected-for-age
values. Our results also permit the interpretation of the
significance of the differences between consecutive measurements
of SMM.

Acknowledgments The authors declare that they have no conflict of interest. The authors of this manuscript certify that they comply with the
ethical guidelines for authorship and publishing in the Journal of Cachexia, Sarcopenia and Muscle [12].

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